# Generalized Canonical Correlation Analysis for Classification

arXiv: https://arxiv.org/abs/1304.7981 · HTML: https://mlsanity.com/paper/1304.7981/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2013.

## Built on by (most cited first, 7 of 7)

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- [Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery](https://mlsanity.com/paper/2405.12797/) (2024)
- [Sparse GCA and Thresholded Gradient Descent](https://mlsanity.com/paper/2107.00371/) (2021)
- [Encoder Embedding for General Graph and Node Classification](https://mlsanity.com/paper/2405.15473/) (2024)
- [The Labeled Multiple Canonical Correlation Analysis for Information Fusion](https://mlsanity.com/paper/2103.00359/) (2021)
- [Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes](https://mlsanity.com/paper/2109.02230/) (2021)
- [Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification](https://mlsanity.com/paper/2402.09281/) (2024)

## Abstract (arXiv)

For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulations and a real data experiment.